The push toward full autonomy in the automotive industry has been bolstered by advancements in Artificial Intelligence, particularly Deep Learning and Machine Learning techniques, which facilitate efficient processing of sensor data for environmental object detection and recognition. Detection of objects with precision and robustness becomes particularly critical on roads, where increased vehicle speeds require fast and reliable decision-making. German highways, known for their high average vehicle speeds, underscore the need for specialized object detection algorithms tailored for such environments. However, these technologies typically depend on large datasets for optimal performance. This research presents a novel approach to detecting and recognizing German highway traffic signs using YOLOv8 while addressing the challenge of limited data availability. By curating high-quality custom datasets for training, the model achieved approximately 90% accuracy on test data and 80% on real-world data, even with reduced dataset size, and demonstrated semi-real-time to real-time performance, highlighting its potential for practical deployment in autonomous driving systems.